SQL Interview Questions for Data Jobs 2026

You know that awful moment when a recruiter says, “There will be a SQL round,” and suddenly every JOIN you ever wrote leaves your brain like it had a better offer somewhere else. If you are applying for data analyst, BI analyst, analytics engineer, data engineer, or junior data scientist roles in 2026, SQL is still the skill hiring teams use to separate “looks good on LinkedIn” from “can actually work with data.”

The annoying part is that SQL interviews are not just about remembering syntax. You need to explain your thinking, spot edge cases, handle messy business questions, and write queries under pressure while someone silently watches you type.

Let’s fix that.

SQL Interview Questions for Data Jobs 2026#

SQL is still the most common technical screen for data jobs because it is practical, fast to test, and painfully honest. A hiring manager at Amazon, Spotify, Uber, Booking.com, Wise, or Stripe can learn a lot from 30 minutes of SQL.

In the US, data analyst roles often sit around $75k to $115k, with senior analysts going past $130k in cities like New York, Seattle, and San Francisco. In Europe, data analyst salaries often range from €45k to €80k, while analytics engineers and data engineers can hit €70k to €110k in places like Amsterdam, Berlin, Dublin, London, and Zurich.

So yes, spending a weekend sharpening SQL can literally change the size of your offer.

What SQL Interviews Look Like in 2026#

Most SQL interviews now follow one of a few formats. The exact tool may change, but the thinking is the same.

You might see:

1. Live coding

   • You share a screen.
   • The interviewer gives you tables and a business question.
   • You write SQL in a shared editor.

2. Take-home SQL task

   • You get a dataset.
   • You answer several questions.
   • You may need to explain assumptions in writing.

3. Platform-based test

   • HackerRank, CoderPad, StrataScratch, DataLemur, or CodeSignal.
   • Usually timed.
   • Often auto-graded.

4. Case-style SQL discussion

   • “How would you find churn?”
   • “How would you measure retention?”
   • “What tables would you need?”

5. SQL plus business thinking

   • Very common for analyst roles.
   • They care less about fancy syntax and more about whether you understand the metric.

For data engineer roles, expect more depth around performance, indexes, partitions, ETL logic, and data quality. For analytics engineer roles, expect dbt-style thinking, modeling, testing, and stakeholder-friendly metrics.

The SQL Skills Employers Actually Test#

Do not waste your prep time memorizing rare functions before you can write clean joins. Most interviews test the same core areas again and again.

Focus on these:

1. SELECT, WHERE, ORDER BY, LIMIT
2. GROUP BY and aggregate functions
3. INNER JOIN, LEFT JOIN, and self joins
4. CASE WHEN
5. Common Table Expressions, also called CTEs
6. Window functions
7. Date handling
8. NULL behavior
9. Subqueries
10. Performance basics
11. Metric logic
12. Data cleaning in SQL

If you can solve realistic questions across those areas, you are in good shape for most analyst roles at companies like Revolut, Zalando, Airbnb, DoorDash, Shopify, and Meta.

Beginner SQL Interview Questions#

These are not “easy” because they are useless. They are easy because they test whether your basics are automatic.

1. Find all customers who signed up in the last 30 days

Example table: users

Columns:

• user_id
• email
• signup_date
• country

A solid answer:

SELECT user_id, email, signup_date, country
FROM users
WHERE signup_date ≥ CURRENT_DATE - INTERVAL '30 days';

In MySQL, this might look like:

SELECT user_id, email, signup_date, country
FROM users
WHERE signup_date ≥ CURDATE() - INTERVAL 30 DAY;

What the interviewer wants to see:

• You understand date filters.
• You do not overcomplicate.
• You know syntax can vary by SQL dialect.

Say something like, “I would confirm the database type because date syntax changes between Postgres, MySQL, BigQuery, and Snowflake.”

That one sentence makes you sound experienced.

2. Count orders by country

Example tables:

orders

• order_id
• user_id
• order_date
• amount

users

• user_id
• country

Query:

SELECT
  u.country,
  COUNT(o.order_id) AS order_count
FROM users u
JOIN orders o
  ON u.user_id = o.user_id
GROUP BY u.country
ORDER BY order_count DESC;

A common mistake is counting user_id instead of order_id, which can still work sometimes but may confuse the business meaning.

You can explain: “I am counting orders, not users. If the question asked for customers who ordered, I would use COUNT(DISTINCT u.user_id).”

3. Find users with no orders

This one tests LEFT JOIN and NULL logic.

SELECT
  u.user_id,
  u.email
FROM users u
LEFT JOIN orders o
  ON u.user_id = o.user_id
WHERE o.order_id IS NULL;

Many candidates accidentally use INNER JOIN here, which removes users without orders. If you remember nothing else, remember this: use LEFT JOIN when you want to keep everything from the first table.

4. Calculate total revenue by month

SELECT
  DATE_TRUNC('month', order_date) AS order_month,
  SUM(amount) AS total_revenue
FROM orders
GROUP BY DATE_TRUNC('month', order_date)
ORDER BY order_month;

In BigQuery:

SELECT
  DATE_TRUNC(order_date, MONTH) AS order_month,
  SUM(amount) AS total_revenue
FROM orders
GROUP BY order_month
ORDER BY order_month;

This question is everywhere because every company cares about revenue trends. Netflix, Etsy, Salesforce, HubSpot, and Deliveroo all need analysts who can group business activity by time.

Intermediate SQL Interview Questions#

This is where most real data analyst interviews live. You are expected to combine joins, filters, aggregations, and business definitions.

5. Find the top 5 products by revenue

Example table: order_items

• order_id
• product_id
• quantity
• unit_price

Query:

SELECT
  product_id,
  SUM(quantity * unit_price) AS revenue
FROM order_items
GROUP BY product_id
ORDER BY revenue DESC
LIMIT 5;

Good candidates mention returns, discounts, taxes, and currency.

You can say: “I am assuming unit_price is the final paid price before tax and that returns are not included. If there is a refunds table, I would subtract refunded amounts.”

That is how you move from “query writer” to “business thinker.”

6. Find customers who made more than 3 purchases

SELECT
  user_id,
  COUNT(order_id) AS purchase_count
FROM orders
GROUP BY user_id
HAVING COUNT(order_id) > 3;

Notice HAVING, not WHERE.

Use WHERE before aggregation. Use HAVING after aggregation.

Tiny rule, big interview points.

7. Calculate average order value by month

SELECT
  DATE_TRUNC('month', order_date) AS order_month,
  SUM(amount) / COUNT(DISTINCT order_id) AS average_order_value
FROM orders
GROUP BY DATE_TRUNC('month', order_date)
ORDER BY order_month;

Some people use AVG(amount), and that can be fine if each row is one order. But if your orders table has multiple rows per order, AVG(amount) breaks.

A strong answer includes: “I would check the grain of the table first. Is one row one order, one item, or one payment event?”

This is a hiring-manager love language.

8. Find duplicate emails

SELECT
  email,
  COUNT(*) AS email_count
FROM users
GROUP BY email
HAVING COUNT(*) > 1;

To return the full user rows:

SELECT *
FROM users
WHERE email IN (
  SELECT email
  FROM users
  GROUP BY email
  HAVING COUNT(*) > 1
);

This question appears in data quality interviews, especially for analytics engineer and data engineer roles. Companies care because duplicate users create broken dashboards, bad CRM campaigns, and angry finance teams.

9. Find daily active users

Example table: events

• event_id
• user_id
• event_time
• event_name

Query:

SELECT
  DATE(event_time) AS event_date,
  COUNT(DISTINCT user_id) AS daily_active_users
FROM events
GROUP BY DATE(event_time)
ORDER BY event_date;

The key is COUNT(DISTINCT user_id), not COUNT(*).

One user can create 50 events in a day. The metric is active users, not event count.

10. Calculate conversion rate from signup to purchase

Example tables:

users

• user_id
• signup_date

orders

• order_id
• user_id
• order_date

Query:

SELECT
  COUNT(DISTINCT o.user_id) * 1.0 / COUNT(DISTINCT u.user_id) AS signup_to_purchase_rate
FROM users u
LEFT JOIN orders o
  ON u.user_id = o.user_id;

Why LEFT JOIN? Because you need all signed-up users in the denominator, including those who never purchased.

A better version for a defined signup cohort:

SELECT
  COUNT(DISTINCT o.user_id) * 1.0 / COUNT(DISTINCT u.user_id) AS signup_to_purchase_rate
FROM users u
LEFT JOIN orders o
  ON u.user_id = o.user_id
WHERE u.signup_date ≥ DATE '2026-01-01'
  AND u.signup_date < DATE '2026-02-01';

You should ask whether conversion means “ever purchased” or “purchased within 7 days of signup.” That question alone can save you from the wrong answer.

Window Function SQL Interview Questions#

Window functions are the big gatekeeper for many mid-level data roles. If you can use ROW_NUMBER, RANK, LAG, and rolling sums, you are much more competitive.

11. Get the latest order for each customer

WITH ranked_orders AS (
  SELECT
    order_id,
    user_id,
    order_date,
    amount,
    ROW_NUMBER() OVER (
      PARTITION BY user_id
      ORDER BY order_date DESC
    ) AS rn
  FROM orders
)
SELECT
  order_id,
  user_id,
  order_date,
  amount
FROM ranked_orders
WHERE rn = 1;

Use ROW_NUMBER when you want exactly one row per group. If ties matter, ask what should happen when two orders share the same timestamp.

12. Rank products by monthly revenue

WITH monthly_product_revenue AS (
  SELECT
    DATE_TRUNC('month', order_date) AS order_month,
    product_id,
    SUM(revenue) AS monthly_revenue
  FROM sales
  GROUP BY DATE_TRUNC('month', order_date), product_id
)
SELECT
  order_month,
  product_id,
  monthly_revenue,
  RANK() OVER (
    PARTITION BY order_month
    ORDER BY monthly_revenue DESC
  ) AS revenue_rank
FROM monthly_product_revenue;

If they ask for only the top 3 per month, wrap it:

WITH monthly_product_revenue AS (
  SELECT
    DATE_TRUNC('month', order_date) AS order_month,
    product_id,
    SUM(revenue) AS monthly_revenue
  FROM sales
  GROUP BY DATE_TRUNC('month', order_date), product_id
),
ranked AS (
  SELECT
    order_month,
    product_id,
    monthly_revenue,
    RANK() OVER (
      PARTITION BY order_month
      ORDER BY monthly_revenue DESC
    ) AS revenue_rank
  FROM monthly_product_revenue
)
SELECT *
FROM ranked
WHERE revenue_rank ≤ 3;

This pattern is extremely interview-friendly: aggregate first, rank second, filter third.

13. Calculate month-over-month revenue growth

WITH monthly_revenue AS (
  SELECT
    DATE_TRUNC('month', order_date) AS order_month,
    SUM(amount) AS revenue
  FROM orders
  GROUP BY DATE_TRUNC('month', order_date)
)
SELECT
  order_month,
  revenue,
  LAG(revenue) OVER (ORDER BY order_month) AS previous_month_revenue,
  (revenue - LAG(revenue) OVER (ORDER BY order_month)) * 1.0
    / LAG(revenue) OVER (ORDER BY order_month) AS mom_growth_rate
FROM monthly_revenue
ORDER BY order_month;

A cleaner version avoids repeating LAG:

WITH monthly_revenue AS (
  SELECT
    DATE_TRUNC('month', order_date) AS order_month,
    SUM(amount) AS revenue
  FROM orders
  GROUP BY DATE_TRUNC('month', order_date)
),
with_previous AS (
  SELECT
    order_month,
    revenue,
    LAG(revenue) OVER (ORDER BY order_month) AS previous_month_revenue
  FROM monthly_revenue
)
SELECT
  order_month,
  revenue,
  previous_month_revenue,
  (revenue - previous_month_revenue) * 1.0
    / NULLIF(previous_month_revenue, 0) AS mom_growth_rate
FROM with_previous
ORDER BY order_month;

The NULLIF protects you from division by zero. Interviewers notice these details.

14. Find a 7-day rolling average of revenue

WITH daily_revenue AS (
  SELECT
    DATE(order_date) AS order_day,
    SUM(amount) AS revenue
  FROM orders
  GROUP BY DATE(order_date)
)
SELECT
  order_day,
  revenue,
  AVG(revenue) OVER (
    ORDER BY order_day
    ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
  ) AS rolling_7_day_avg
FROM daily_revenue
ORDER BY order_day;

This question is common for marketplace, subscription, and e-commerce companies. Think Airbnb, Vinted, Uber, Instacart, ASOS, and Shopify.

Advanced SQL Interview Questions for Data Jobs#

Advanced does not always mean weird. It usually means the business question is layered and the data has traps.

15. Calculate customer retention by monthly cohort

This is a classic for product analytics roles.

Tables:

users

• user_id
• signup_date

events

• user_id
• event_time
• event_name

Query:

WITH cohorts AS (
  SELECT
    user_id,
    DATE_TRUNC('month', signup_date) AS cohort_month
  FROM users
),
activity AS (
  SELECT DISTINCT
    user_id,
    DATE_TRUNC('month', event_time) AS activity_month
  FROM events
),
cohort_activity AS (
  SELECT
    c.cohort_month,
    a.activity_month,
    c.user_id
  FROM cohorts c
  JOIN activity a
    ON c.user_id = a.user_id
),
cohort_sizes AS (
  SELECT
    cohort_month,
    COUNT(DISTINCT user_id) AS cohort_size
  FROM cohorts
  GROUP BY cohort_month
)
SELECT
  ca.cohort_month,
  ca.activity_month,
  COUNT(DISTINCT ca.user_id) AS active_users,
  cs.cohort_size,
  COUNT(DISTINCT ca.user_id) * 1.0 / cs.cohort_size AS retention_rate
FROM cohort_activity ca
JOIN cohort_sizes cs
  ON ca.cohort_month = cs.cohort_month
GROUP BY ca.cohort_month, ca.activity_month, cs.cohort_size
ORDER BY ca.cohort_month, ca.activity_month;

A strong candidate adds a cohort age column:

DATE_PART('month', AGE(activity_month, cohort_month)) AS months_since_signup

The exact function depends on your SQL dialect, but the idea matters: compare activity month to signup month.

16. Find users who purchased in two consecutive months

WITH monthly_purchases AS (
  SELECT DISTINCT
    user_id,
    DATE_TRUNC('month', order_date) AS purchase_month
  FROM orders
),
with_previous AS (
  SELECT
    user_id,
    purchase_month,
    LAG(purchase_month) OVER (
      PARTITION BY user_id
      ORDER BY purchase_month
    ) AS previous_purchase_month
  FROM monthly_purchases
)
SELECT DISTINCT user_id
FROM with_previous
WHERE purchase_month = previous_purchase_month + INTERVAL '1 month';

This tests date logic plus window functions.

Mention that month arithmetic varies by SQL dialect. In BigQuery, you might use DATE_ADD(previous_purchase_month, INTERVAL 1 MONTH).

17. Identify churned subscribers

Example table: subscriptions

• user_id
• start_date
• end_date
• plan

If churn means subscription ended in the previous month:

SELECT
  user_id,
  plan,
  end_date
FROM subscriptions
WHERE end_date ≥ DATE_TRUNC('month', CURRENT_DATE) - INTERVAL '1 month'
  AND end_date < DATE_TRUNC('month', CURRENT_DATE);

But in real interviews, churn is a definition problem.

Ask:

1. Does churn mean canceled, expired, or inactive?
2. Do we count users who later reactivated?
3. Are free trials included?
4. Is churn measured by users, accounts, seats, or revenue?
5. What time zone defines the month?

For subscription companies like Spotify, Adobe, Notion, Canva, and The New York Times, these definitions affect millions in reported metrics.

SQL Questions for Data Analyst Interviews#

Data analyst SQL rounds are usually business-heavy. You need readable SQL and smart assumptions.

Expect questions like:

18. Which marketing channel has the highest conversion rate?

Tables:

users

• user_id
• signup_date
• marketing_channel

orders

• order_id
• user_id
• order_date

Query:

SELECT
  u.marketing_channel,
  COUNT(DISTINCT o.user_id) * 1.0 / COUNT(DISTINCT u.user_id) AS conversion_rate
FROM users u
LEFT JOIN orders o
  ON u.user_id = o.user_id
GROUP BY u.marketing_channel
ORDER BY conversion_rate DESC;

Then say: “I would also check sample size, because a channel with 2 users and 1 purchase has 50% conversion but is not necessarily the best channel.”

That is exactly the kind of practical thinking teams want.

19. What percentage of revenue comes from repeat customers?

WITH customer_orders AS (
  SELECT
    user_id,
    order_id,
    order_date,
    amount,
    ROW_NUMBER() OVER (
      PARTITION BY user_id
      ORDER BY order_date
    ) AS order_number
  FROM orders
)
SELECT
  SUM(CASE WHEN order_number > 1 THEN amount ELSE 0 END) * 1.0
    / SUM(amount) AS repeat_customer_revenue_share
FROM customer_orders;

Clarify whether “repeat customer revenue” means revenue from second and later orders, or all revenue from customers who have ever ordered more than once. Those are different.

20. Find products with declining sales for three months in a row

This is more advanced but realistic.

WITH monthly_sales AS (
  SELECT
    product_id,
    DATE_TRUNC('month', order_date) AS sales_month,
    SUM(amount) AS revenue
  FROM sales
  GROUP BY product_id, DATE_TRUNC('month', order_date)
),
with_lags AS (
  SELECT
    product_id,
    sales_month,
    revenue,
    LAG(revenue, 1) OVER (
      PARTITION BY product_id
      ORDER BY sales_month
    ) AS prev_month_revenue,
    LAG(revenue, 2) OVER (
      PARTITION BY product_id
      ORDER BY sales_month
    ) AS two_months_ago_revenue
  FROM monthly_sales
)
SELECT
  product_id,
  sales_month,
  revenue,
  prev_month_revenue,
  two_months_ago_revenue
FROM with_lags
WHERE revenue < prev_month_revenue
  AND prev_month_revenue < two_months_ago_revenue;

This is the sort of question you might see in retail, SaaS, or marketplace interviews.

SQL Questions for Data Engineer Interviews#

Data engineer SQL interviews often care about correctness at scale. You still need joins and windows, but performance and reliability matter more.

21. How would you remove duplicates while keeping the latest record?

Example table: customer_events

• event_id
• customer_id
• event_type
• updated_at

WITH ranked AS (
  SELECT
    *,
    ROW_NUMBER() OVER (
      PARTITION BY customer_id, event_type
      ORDER BY updated_at DESC
    ) AS rn
  FROM customer_events
)
SELECT *
FROM ranked
WHERE rn = 1;

For a production pipeline, explain:

• What makes a duplicate?
• What timestamp should win?
• Should ties be deterministic?
• Is this a one-time cleanup or recurring job?
• Should bad records go to a quarantine table?

This is very relevant for jobs at Snowflake, Databricks, Google Cloud, AWS, and fintech companies that handle messy event streams.

22. How would you improve a slow SQL query?

Give a practical checklist:

1. Check the query plan.
2. Filter early where possible.
3. Avoid selecting unused columns.
4. Join on indexed or clustered keys.
5. Reduce data before joining large tables.
6. Watch for many-to-many joins.
7. Use partitions for date filters.
8. Pre-aggregate when the business allows it.
9. Avoid functions on indexed columns in filters.
10. Check table statistics.

Example bad pattern:

WHERE DATE(order_timestamp) = DATE '2026-01-15'

Better pattern:

WHERE order_timestamp ≥ TIMESTAMP '2026-01-15 00:00:00'
  AND order_timestamp < TIMESTAMP '2026-01-16 00:00:00'

Why? The second version can usually use the timestamp column more efficiently.

23. What is the difference between WHERE and HAVING?

Say it simply:

• WHERE filters rows before grouping.
• HAVING filters grouped results after aggregation.

Example:

SELECT
  user_id,
  COUNT(*) AS order_count
FROM orders
WHERE order_date ≥ DATE '2026-01-01'
GROUP BY user_id
HAVING COUNT(*) ≥ 5;

This means: only count orders from 2026 onward, then return users with at least 5 of those orders.

SQL Questions for Analytics Engineer Interviews#

Analytics engineering roles sit between data analyst and data engineer. Think dbt, Snowflake, BigQuery, Redshift, Looker, Mode, Hex, and metric definitions.

24. How would you model orders and order items?

A good answer mentions grains.

For example:

• stg_orders: one row per raw order.
• stg_order_items: one row per product in an order.
• dim_customers: one row per customer.
• dim_products: one row per product.
• fct_orders: one row per order.
• fct_order_items: one row per order item.

Then say what each table is for:

• Use fct_orders for order count, total order revenue, average order value.
• Use fct_order_items for product sales, basket analysis, category revenue.
• Use dim_customers for segments, countries, acquisition channels.
• Use dim_products for categories, brands, margins.

This answer shows you know SQL is not just queries. It is also data structure.

25. What tests would you add to a customer table?

Good tests:

1. customer_id is unique.
2. customer_id is not null.
3. email has valid format if required.
4. created_at is not in the future.
5. country is in an accepted list.
6. Foreign keys match source systems.
7. No duplicate active accounts unless allowed.

If you mention dbt tests like unique, not_null, accepted_values, and relationships, you get bonus points.

Tricky SQL Concepts Interviewers Love#

Some questions are not hard because the SQL is long. They are hard because the logic is slippery.

NULLs

Remember:

• NULL = NULL is not true.
• Use IS NULL, not = NULL.
• Aggregates often ignore NULLs.
• COUNT(*) counts rows.
• COUNT(column_name) counts non-null values.

Example:

SELECT COUNT(*) AS total_rows,
       COUNT(email) AS rows_with_email
FROM users;

COUNT DISTINCT

COUNT(DISTINCT user_id) is often needed for user metrics. But it can be expensive on huge datasets.

If asked about scale, mention approximate distinct counts in systems like BigQuery or HyperLogLog-style functions where exact precision is not required.

JOIN duplication

If you join orders to order items, one order becomes multiple rows. This can inflate revenue, counts, and averages.

Always ask: “What is the grain of this table?”

That question should be tattooed on every analyst’s coffee mug.

Time zones

A day in UTC might not be a business day in New York, Berlin, or Singapore.

If the company operates internationally, say: “I would confirm the reporting time zone before grouping by date.”

This matters a lot at companies like Airbnb, Uber, TikTok, Meta, and Booking.com.

How to Answer SQL Interview Questions Out Loud#

Writing the correct query is great. Explaining it clearly is what gets you hired.

Use this structure:

1. Repeat the business question

   • “We want daily active users by calendar day.”

2. Clarify the metric

   • “Does active mean any event, login only, or a specific event type?”

3. Confirm the table grain

   • “Is each row one event?”

4. Talk through the approach

   • “I will group events by date and count distinct users.”

5. Write the query

   • Keep aliases clean.
   • Use CTEs when it improves readability.

6. Check edge cases

   • NULLs.
   • duplicates.
   • time zones.
   • late-arriving data.
   • test users.

7. Explain the result

   • “This returns one row per day with the number of unique active users.”

This makes you look calm even if your brain is doing cartwheels.

SQL Interview Prep Plan for 7 Days#

If your interview is next week, do not panic-scroll 400 random questions. Follow a focused plan.

Day 1: Basics and joins

Practice:

• INNER JOIN
• LEFT JOIN
• GROUP BY
• HAVING
• COUNT DISTINCT
• NULL filters

Goal: solve simple questions without checking notes.

Day 2: Dates and business metrics

Practice:

• Revenue by month
• Active users by day
• Conversion rate
• Average order value
• Churn windows

Goal: get comfortable with time filters.

Day 3: Window functions

Practice:

• ROW_NUMBER
• RANK
• DENSE_RANK
• LAG
• LEAD
• Rolling averages

Goal: stop being scared of OVER (PARTITION BY...).

Day 4: Product analytics

Practice:

• Retention
• Cohorts
• funnels
• repeat purchases
• activation metrics

Goal: connect SQL to product questions.

Day 5: Data quality

Practice:

• duplicates
• missing values
• mismatched joins
• orphan records
• invalid dates
• test accounts

Goal: think like someone who has seen real data, not textbook data.

Day 6: Timed practice

Set a 45-minute timer.

Do:

1. One easy query.
2. Two medium queries.
3. One window function query.
4. One business metric explanation.

Goal: build interview speed.

Day 7: Mock interview

Say your thinking out loud. Record yourself if you can stand it.

Yes, it feels awkward. So does bombing a live SQL screen because you never practiced speaking while typing.

Common SQL Interview Mistakes#

You can avoid most bad SQL interview moments with a few habits.

Watch out for:

1. Starting too fast

   • Ask clarifying questions first.

2. Ignoring table grain

   • One row per what? User, event, item, order?

3. Using INNER JOIN when you need LEFT JOIN

   • This ruins denominators.

4. Counting rows instead of users

   • COUNT(*) and COUNT(DISTINCT user_id) are not the same.

5. Forgetting NULL behavior

   • Especially in filters and joins.

6. Writing one giant query

   • CTEs are easier to read and debug.

7. Not checking for duplicates

   • Join duplication is a silent killer.

8. Not explaining assumptions

   • Your assumptions matter as much as your syntax.

9. Forgetting SQL dialect differences

   • Postgres, MySQL, Snowflake, BigQuery, and SQL Server all have quirks.

10. Panicking after one syntax error

• Everyone has syntax errors. Stay calm and fix them.

What Hiring Teams Expect by Seniority#

Not every role expects the same SQL level.

Junior data analyst

Expected:

• Basic joins.
• Aggregations.
• Simple date filters.
• Clean explanations.
• Basic dashboards.

Salary range examples:

• US: $60k to $85k.
• Germany: €40k to €55k.
• Netherlands: €42k to €60k.
• UK: £30k to £45k.

Mid-level data analyst

Expected:

• Window functions.
• Business metrics.
• Funnel analysis.
• Retention basics.
• Better stakeholder thinking.

Salary range examples:

• US: $85k to $120k.
• Ireland: €55k to €75k.
• Germany: €55k to €80k.
• UK: £45k to £70k.

Senior data analyst or analytics engineer

Expected:

• Complex SQL.
• Data modeling.
• Metric ownership.
• Performance awareness.
• Strong communication.

Salary range examples:

• US: $120k to $160k.
• Netherlands: €75k to €105k.
• Germany: €75k to €100k.
• Switzerland: CHF 110k to CHF 150k.

Data engineer

Expected:

• SQL optimization.
• Pipeline logic.
• Incremental loads.
• Data quality.
• Warehousing concepts.
• Python or Spark often helps too.

Salary range examples:

• US: $115k to $180k.
• UK: £70k to £110k.
• Germany: €75k to €115k.
• Ireland: €75k to €110k.

Best Resources to Practice SQL in 2026#

Use a mix of interview questions and real business-style datasets.

Good places to practice:

1. DataLemur

   • Great for product and tech-company SQL questions.

2. StrataScratch

   • Strong for realistic analyst questions.

3. HackerRank

   • Good for timed basics.

4. LeetCode Database

   • Useful for pattern practice.

5. Mode SQL Tutorial

   • Friendly for beginners.

6. Google BigQuery public datasets

   • Great if you want real-world messy data.

7. Kaggle datasets

   • Good for building portfolio projects.

Do not just read solutions. Type them. Break them. Fix them. That is how SQL sticks.

Final SQL Interview Checklist#

Before your interview, review this:

• Can I explain INNER JOIN vs LEFT JOIN?
• Can I use GROUP BY and HAVING correctly?
• Can I calculate conversion rate with the right denominator?
• Can I use ROW_NUMBER to get the latest row per user?
• Can I use LAG for month-over-month change?
• Can I handle NULLs safely?
• Can I explain table grain?
• Can I spot duplicate inflation after joins?
• Can I ask good metric questions?
• Can I speak while writing SQL?

If yes, you are not just “studying SQL.” You are preparing like someone who can do the job.

SQL interviews in 2026 are not about being a syntax robot. They are about proving you can turn messy tables into business answers without quietly wrecking the numbers.

And hey, before you send out another data job application, make sure your resume is not getting filtered before anyone sees your SQL skills. Run it through JobRise’s free ATS checker here: https://jobrise.io/en/free-ats-checker/